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SentencePiece python wrapper

pip install sentencepiece

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    SentencePiece Python Wrapper

    Python wrapper for SentencePiece. This API will offer the encoding, decoding and training of Sentencepiece.

    Build and Install SentencePiece

    For Linux (x64/i686), macOS, and Windows(win32/x64) environment, you can simply use pip command to install SentencePiece python module.

    % pip install sentencepiece
    

    To build and install the Python wrapper from source, try the following commands to build and install wheel package.

    % git clone https://github.com/google/sentencepiece.git 
    % cd sentencepiece
    % mkdir build
    % cd build
    % cmake .. -DSPM_ENABLE_SHARED=OFF -DCMAKE_INSTALL_PREFIX=./root
    % make install
    % cd ../python
    % python setup.py bdist_wheel
    % pip install dist/sentencepiece*.whl
    

    If you don’t have write permission to the global site-packages directory or don’t want to install into it, please try:

    % python setup.py install --user
    

    Usage

    See this google colab page to run sentencepiece interactively.

    Segmentation

    % python
    >>> import sentencepiece as spm
    >>> sp = spm.SentencePieceProcessor(model_file='test/test_model.model')
    
    >>> sp.encode('This is a test')
    [284, 47, 11, 4, 15, 400]
    
    >>> sp.encode(['This is a test', 'Hello world'], out_type=int)
    [[284, 47, 11, 4, 15, 400], [151, 88, 21, 887]]
    
    >>> sp.encode_as_ids(['This is a test', 'Hello world'])
    [[284, 47, 11, 4, 15, 400], [151, 88, 21, 887]]
    
    >>> sp.encode('This is a test', out_type=str)
    ['▁This', '▁is', '▁a', '▁', 't', 'est']
    
    >>> sp.encode(['This is a test', 'Hello world'], out_type=str)
    [['▁This', '▁is', '▁a', '▁', 't', 'est'], ['▁He', 'll', 'o', '▁world']]
    
    >>> sp.encode_as_pieces(['This is a test', 'Hello world'])
    [['▁This', '▁is', '▁a', '▁', 't', 'est'], ['▁He', 'll', 'o', '▁world']]
    
    >>> proto = sp.encode('This is a test', out_type='immutable_proto')
    >>> for n in proto.pieces:
    ...     print('piece="{}" surface="{}" id={} begin={} end={}'.format(n.piece, n.surface, n.id, n.begin, n.end))
    ... 
    piece="▁This" surface="This" id=284 begin=0 end=4
    piece="▁is" surface=" is" id=47 begin=4 end=7
    piece="▁a" surface=" a" id=11 begin=7 end=9
    piece="▁" surface=" " id=4 begin=9 end=10
    piece="t" surface="t" id=15 begin=10 end=11
    piece="est" surface="est" id=400 begin=11 end=14
    
    >>> [[x.id for x in proto.pieces], [x.piece for x in proto.pieces], [x.begin for x in proto.pieces], [x.end for x in proto.pieces]]
    [[284, 47, 11, 4, 15, 400], ['▁This', '▁is', '▁a', '▁', 't', 'est'], [0, 4, 7, 9, 10, 11], [4, 7, 9, 10, 11, 14]]
    
    >>> proto2 = sp.encode_as_immutable_proto('This is a test')
    >>> proto2 == proto
    True
    
    >>> for _ in range(10):
    ...     sp.encode('This is a test', out_type=str, enable_sampling=True, alpha=0.1, nbest_size=-1)
    ... 
    ['▁', 'This', '▁', 'is', '▁a', '▁', 't', 'e', 'st']
    ['▁T', 'h', 'i', 's', '▁is', '▁a', '▁', 'te', 's', 't']
    ['▁T', 'h', 'is', '▁', 'is', '▁', 'a', '▁', 't', 'est']
    ['▁', 'This', '▁is', '▁', 'a', '▁', 't', 'e', 'st']
    ['▁', 'This', '▁', 'is', '▁', 'a', '▁', 't', 'e', 's', 't']
    ['▁This', '▁is', '▁a', '▁', 'te', 's', 't']
    ['▁This', '▁is', '▁', 'a', '▁', 't', 'e', 'st']
    ['▁', 'T', 'h', 'is', '▁', 'is', '▁', 'a', '▁', 'te', 'st']
    ['▁', 'This', '▁', 'i', 's', '▁a', '▁', 't', 'e', 'st']
    ['▁This', '▁', 'is', '▁a', '▁', 't', 'est']
    
    >> sp.nbest_encode('This is a test', nbest_size=5, out_type=str)
    [['▁This', '▁is', '▁a', '▁', 't', 'est'], 
    ['▁This', '▁is', '▁a', '▁', 'te', 'st'], 
    ['▁This', '▁is', '▁a', '▁', 'te', 's', 't'],
    ['▁This', '▁is', '▁a', '▁', 't', 'e', 'st'],
    ['▁This', '▁is', '▁a', '▁', 't', 'es', 't']]
    
    >>> sp.sample_encode_and_score('This is a test', num_samples=5, alpha=0.1, out_type=str, wor=True)
    [(['▁This', '▁', 'i', 's', '▁a', '▁', 'te', 's', 't'], -3.043105125427246),
    (['▁This', '▁', 'i', 's', '▁a', '▁', 'te', 'st'], -2.8475849628448486),
    (['▁', 'This', '▁is', '▁', 'a', '▁', 'te', 'st'], -3.043248176574707),
    (['▁', 'This', '▁is', '▁a', '▁', 't', 'e', 'st'], -2.87727689743042),
    (['▁', 'This', '▁', 'i', 's', '▁', 'a', '▁', 't', 'est'], -3.6284031867980957)]
    
    >>> sp.decode([284, 47, 11, 4, 15, 400])
    'This is a test'
    
    >>> sp.decode([[284, 47, 11, 4, 15, 400], [151, 88, 21, 887]])
    ['This is a test', 'Hello world']
    
    >>> proto = sp.decode([284, 47, 11, 4, 15, 400], out_type='immutable_proto') 
    >>> proto.text
    'This is a test'
    
    >>> sp.decode(['▁', 'This', '▁', 'is', '▁a', '▁', 't', 'e', 'st'])
    'This is a test'
    
    >>> sp.decode([['▁This', '▁is', '▁a', '▁', 't', 'est'], ['▁He', 'll', 'o', '▁world']])
    ['This is a test', 'Hello world']
    
    >>> sp.get_piece_size()
    1000
    
    >>> sp.id_to_piece(2)
    '</s>'
    
    >>> sp.id_to_piece([2, 3, 4])
    ['</s>', '\r', '▁']
    
    >>> sp.piece_to_id('<s>')
    1
    
    >>> sp.piece_to_id(['</s>', '\r', '▁'])
    [2, 3, 4]
    
    >>> len(sp)
    1000
    
    >>> sp['</s>']
    2
    

    Model Training

    Training is performed by passing parameters of spm_train to SentencePieceTrainer.train() function.

    >>> import sentencepiece as spm
    >>> spm.SentencePieceTrainer.train(input='test/botchan.txt', model_prefix='m', vocab_size=1000, user_defined_symbols=['foo', 'bar'])
    sentencepiece_trainer.cc(73) LOG(INFO) Starts training with : 
    trainer_spec {
      input: test/botchan.txt
      .. snip
    unigram_model_trainer.cc(500) LOG(INFO) EM sub_iter=1 size=1188 obj=10.2839 num_tokens=32182 num_tokens/piece=27.0892
    unigram_model_trainer.cc(500) LOG(INFO) EM sub_iter=0 size=1100 obj=10.4269 num_tokens=33001 num_tokens/piece=30.0009
    unigram_model_trainer.cc(500) LOG(INFO) EM sub_iter=1 size=1100 obj=10.4069 num_tokens=33002 num_tokens/piece=30.0018
    trainer_interface.cc(595) LOG(INFO) Saving model: m.model
    trainer_interface.cc(619) LOG(INFO) Saving vocabs: m.vocab
    >>>
    

    Training without local filesystem

    Sentencepiece trainer can receive any iterable object to feed training sentences. You can also pass a file object (instance with write() method) to emit the output model to any devices. These features are useful to run sentencepiece on environment that have limited access to the local file system (e.g., Google colab.)

    import urllib.request
    import io
    import sentencepiece as spm
    
    # Loads model from URL as iterator and stores the model to BytesIO.
    model = io.BytesIO()
    with urllib.request.urlopen(
        'https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt'
    ) as response:
      spm.SentencePieceTrainer.train(
          sentence_iterator=response, model_writer=model, vocab_size=1000)
    
    # Serialize the model as file.
    # with open('out.model', 'wb') as f:
    #   f.write(model.getvalue())
    
    # Directly load the model from serialized model.
    sp = spm.SentencePieceProcessor(model_proto=model.getvalue())
    print(sp.encode('this is test'))